Participatory, Virologic, and Wastewater Surveillance Data to Assess Underestimation of COVID-19 Incidence, Germany, 2020–2024

Using participatory, virologic, and wastewater surveillance systems, we estimated when and to what extent reported data of adult COVID-19 cases underestimated COVID-19 incidence in Germany. We also examined how case underestimation evolved over time. Our findings highlight how community-based surveillance systems can complement official notification systems for respiratory disease dynamics.

We used an underestimation factor (UEF) to express sensitivity of GNS-I by GW-VPR-I (UEF GW-VPR-I ) and GW-SR-I (UEF GW-SR-I ), which we calculated as the weekly ratio of smoothed GW-VPR-I and GW-SR-I relative to nonsmoothed GNS-I (Table ; Figure ).In addition, we gathered information on dates of pandemic related PHSM.
We identified 4 major sensitivity phases of GNS-I and estimated a segmented linear regression to specify 3 breakpoints (7,8).We calculated a common piecewise trendline of the smoothed UEF GW-VPR-I and UEF GW-SR-I data (Figure , panel B).During phase 1, CW40/2020-CW10/2022, the linear trend of UEF GW-VPR-I varied ≈1.1-1.5, indicating close agreement between GNS-I and GW-VPR-I.Two COVID-19 waves, driven by Omicron BA.1, peaking in CW05/2022, and BA.2, peaking in CW11/2022, were still well captured by GNS-I.During that time, many workplaces, hospitals, nursing homes, kindergartens, and schools tested regularly for SARS-CoV-2.However, during CW10/2022-CW17/2022, regular testing at workplaces and  One limitation of our study is the incongruence among the indicators; GNS-I includes data for illnesses and asymptomatic infections, whereas GW-VPR-I and GW-SR-I only estimate illness incidence.However, because the information on presence or absence of symptoms is not always available in GNS-I data, deriving a pure COVID-19 incidence from GNS-I is not possible.Another limitation is that WWS provides viral load per liter from all population age groups, and neither incidence nor prevalence data are collected; whether the shedding properties of variants differ enough to substantially modify the viral load detected in wastewater is unknown.Last, the association of sensitivity phases and PHSM is only descriptive and ecologic in nature.

Conclusions
Assessing the timing and degree of COVID-19 underestimation is crucial for interpretating notification system data.Until the first half of 2022, serosurveys among blood donors in Germany estimated the degree of underestimation at ≈1.5 of GNS-I, comparing well with the common piecewise trendline of UEF GW-VPR-I in the same timeframe (UEF GW-SR-I started from CW27/2022) (9).Other than cross-sectional serosurveys (9)(10)(11), approaches to estimate underestimation included analysis of fatality rates and death tolls (12,13), and a multiplier model that used reported laboratory-confirmed data as a starting point (14).However, none of those approaches compared weekly notification rates and, thus, cannot pinpoint sensitivity breakpoints.We compared weekly national notifiable COVID-19 incidence to 2 independent indicators estimating populationlevel incidence, and our findings are supported by WWS results.
We identified 2 major sensitivity breakpoints, demonstrating that PHSM introductions or cessations might have directly affected the changing sensitivity of notification data.Ending systematic testing in workplaces and schools (first breakpoint) and ending no-cost testing (second breakpoint) likely contributed to the decrease of national notifiable disease surveillance system sensitivity.The close agreement between WWS and GrippeWeb-derived incidence indicators suggests that SARS-CoV-2 wastewater data are useful for indicating trends in infection waves.
Although population-level immunity could influence the probability of persons testing COVID-19positive to some degree, immunity mainly protects against severe disease but does not necessarily prevent infection or illness.For example, the high estimated COVID-19 incidence at the end of 2023 had weekly incidences of >2% (Figure).
As Germany transitioned from the pandemic to endemic phase and implemented a stepwise reduction in testing, GNS-I became less capable of reflecting actual COVID-19 incidence.Our study results stress the value of additional community-based and wastewater surveillance systems to complement official notification systems (15).Community-based surveillance can describe the epidemiologic situation, particularly when PHSM, such as testing policies, are lifted and testing access decreases.Thus, systems like GrippeWeb (and wastewater surveillance) will be increasingly crucial, especially for respiratory diseases of epidemic and pandemic potential.

Figure .
Figure.Incidence and underestimation factors in a study of participatory, virologic, and wastewater surveillance data to assess underestimation of COVID-19 incidence, Germany, 2020-2024.A) Smoothed and unsmoothed surveillance data on COVID-19 incidence (cases/100,000 adult population) compared with wastewater viral load.SARS-CoV-2 variant phases in Germany are labeled.B) Two different UEFs plus common piecewise trendline of smoothed UEF and timeframes for phases calendar week 40 of 2020 through calendar week 4 of 2024.Vertical lines mark the breakpoints between COVID-19 phases with different degrees of underestimation.GNS-I, incidence from German notification system; GW-SR-I, GrippeWeb self-reported incidence; GW-VPR-I, GrippeWeb and virologic positivity rate incidence; SC2-VL-WW, aggregated SARS-CoV-2 viral load in wastewater; UEF, underestimation factor.

Table .
List of surveillance systems and indicators used for participatory, virologic, and wastewater surveillance data to assess underestimation of COVID-19 incidence, Germany, 2020-2024* COVID-19 comparison indicator using WWS system and expressed as the number of SARS-CoV-2 gene fragments per liter in wastewater UEF Underestimation factor Two underestimation factors were calculated as an indicator to estimate the sensitivity of GNS-derived COVID-19 incidence with the help of GW and VSS surveillance data